• Journal of Korean Society for Atmospheric Environment
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• v.31 no.5
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• pp.437-448
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• 2015

The purpose of this study is to present the source contribution of the fine particles ($PM_{2.5}$) in Chungju area using the CMB (chemical mass balance) method throughout the four seasons in Korea. The Chungju's annual average level of $PM_{2.5}$ was $48.2{\mu}g/m^3$, which exceeded two times higher than standard air quality. Among these particles, the soluble ionic compounds represent 54.2% of fine particle mass. Additionally, the OC concentration in Chungju stayed similar to other domestic cities, while the EC concentration decreased significantly compared to other domestic/international cities. The concentration of sulfur represented the highest composition (8%) among the fine particle compounds. According to the CMB results, the general trend of the $PM_{2.5}$ mass contributors was the following: secondary aerosols (50.5%: ammonium sulfate 26.5% and ammonium nitrate 24.0%) > gasoline vehicle (18.3%) > biomass burning (11.0%) > industrial boiler (6.0%) > diesel vehicles (4.4%). The contribution of the secondary aerosols was the main cause than others. This impact is assumed to be emitted from air pollutants of urban cities or neighbor countries such as China.

• Journal of Environmental Science International
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• v.31 no.7
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• pp.637-652
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• 2022

In this study, the contributions of emissions (foreign and domestic) and atmospheric physical and chemical processes to PM_2.5 concentrations were evaluated during a high PM_2.5 episode (March 24-26, 2018) observed on the Jeju Island in the spring of 2018. These analyses were performed using the community multi-scale air quality (CMAQ) modeling system using the brute-force method and integrated process rate (IPR) analysis, respectively. The contributions of domestic emissions from South Korea (41-45%) to PM_2.5 on the Jeju Island were lower than those (81-89%) of long-range transport (LRT) from China. The substantial contribution of LRT was also confirmed in conjunction with the air mass trajectory analysis, indicating that the frequency of airflow from China (58-62% of all trajectories) was higher than from other regions (28-32%) (e.g., South Korea). These results imply that compared to domestic emissions, emissions from China have a stronger impact than domestic emissions on the high PM_2.5 concentrations in the study area. From the IPR analysis, horizontal transport contributed substantially to PM_2.5 concentrations were dominant in most of the areas of the Jeju Island during the high PM_2.5 episode, while the aerosol process and vertical transport in the southern areas largely contributed to higher PM_2.5 concentrations.

• Journal of Korean Society for Atmospheric Environment
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• v.32 no.1
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• pp.82-99
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• 2016

This study was carried out to understand the regional contribution of Particulate Matter (PM) emissions from East Asia ($82^{\circ}{\sim}149^{\circ}E$, $18^{\circ}{\sim}53^{\circ}N$) to Seoul during high concentration period in February 2014. The Community Multi-scale Air Quality (CMAQ) version 5.0.2 with Decoupled Direct Method (DDM) was used to analyze levels of contributions over Seoul. In order to validate model performance of the CMAQ, predicted PM and its chemical species concentrations were compared to observations in China and Seoul. Model predictions could depict the daily and hourly variations of observed PM. The calculated PM concentrations, however, had a tendency of underestimation. The discrepancies are due to uncertainties of meteorological data, emission inventories and CMAQ model itself. The high PM concentration in Seoul was induced by stationary anticyclone over the West Coast of Korea during 24 to 27 February. The DDM in CMAQ was used to analyze the contributions of emissions from East Asia on Seoul during this PM episode. $PM_{10}$ concentration in Seoul is contributed by 39.77%~53.19% from China industrial and urban region, 15.37%~37.10% from South Korea, and 9.03%~18.05% North Korea. These indicate that $PM_{10}$ concentrations in Seoul during the episode period are dominated by long-range transport from China region as well as domestic sources. It was also found that the largest contribution region in China were Shandong peninsula during the PM event period.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.5
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• pp.497-514
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• 2017

This study quantitatively analyzes the effects of emission inventory choices on the simulated particulate matter (PM) concentrations and the domestic/foreign contributions in the Seoul Metropolitan Area (SMA) with an air quality forecasting system. The forecasting system is composed of Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Community Multi-Scale Air Quality (CMAQ). Different domestic and foreign emission inventories were selectively adopted to set up four sets of emissions inputs for air quality simulations in this study. All modeling cases showed that model performance statistics satisfied the criteria levels (correlation coefficient >0.7, fractional error <50%) suggested by previous studies. Notwithstanding the apparently good model performance of total PM concentrations by all emission cases, annual average concentrations of simulated total PM concentrations varied up to $20{\mu}g/m^3$ (160%) depending on the combination of emission inventories. In detail, the difference in simulated annual average concentrations of the primary PM coarse (PMC) was up to $25.2{\mu}g/m^3$ (6.5 times) compared with other cases. Furthermore, model performance analyses on PM species showed that the difference in the simulated primary PMC led to gross model overestimation in general, which indicates that the primary PMC emissions need to be improved. The contribution analysis using model direct outputs indicated that the domestic contributions to the annual average PM concentrations in the SMA vary from 44% to 67%. To account for the uncertainty of the simulated concentration, the contribution correction factor method proposed by Bae et al. (2017) was applied, which resulted in converged contributions(from 48% to 57%). We believe this study shows that it is necessary to improve the simulated concentrations of PM components in order to enhance the accuracy of the forecasting model. It is deemed that these improvements will provide more accurate contribution results.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.5
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• pp.445-457
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• 2017

In this study, we developed an approach to better account for uncertainties in estimated contributions from fine particulate matter ($PM_{2.5}$) modeling. Our approach computes a Concentration Correction Factor (CCF) which is a ratio of observed concentrations to baseline model concentrations. We multiply modeled direct contribution estimates with CCF to obtain revised contributions. Overall, the modeling system showed reasonably good performance, correlation coefficient R of 0.82 and normalized mean bias of 2%, although the model underestimated some PM species concentrations. We also noticed that model biases vary seasonally. We compared contribution estimates of major source sectors before and after applying CCFs. We observed that different source sectors showed variable magnitudes of sensitivities to the CCF application. For example, the total primary $PM_{2.5}$ contribution was increased $2.4{\mu}g/m^3$ or 63% after the CCF application. Out of a $2.4{\mu}g/m^3$ increment, line sources and area source made up $1.3{\mu}g/m^3$ and $0.9{\mu}g/m^3$ which is 92% of the total contribution changes. We postulated two major reasons for variations in estimated contributions after the CCF application: (1) monthly variability of unadjusted contributions due to emission source characteristics and (2) physico-chemical differences in environmental conditions that emitted precursors undergo. Since emissions-to-$PM_{2.5}$ concentration conversion rate is an important piece of information to prioritize control strategy, we examined the effects of CCF application on the estimated conversion rates. We found that the application of CCFs can alter the rank of conversion efficiencies of source sectors. Finally, we discussed caveats of our current approach such as no consideration of ion neutralization which warrants further studies.

• Journal of Environmental Impact Assessment
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• v.28 no.3
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• pp.183-200
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• 2019

In this study, the characteristics of seasonal $PM_{2.5}$ behavior in South Korea and other Northeast Asian regions were analyzed by using the $PM_{2.5}$ ground measurement data, weather data, WRF and CMAQ models. Analysis of seasonal $PM_{2.5}$ behavior in Northeast Asia showed that $PM_{2.5}$ concentration at 6 IMS sites in South Korea was increased by long-distance transport and atmospheric congestion, or decreased by clean air inflow due to seasonal weather characteristics. As a result of analysis by applying BFM to air quality model, the contribution from foreign countries dominantly influenced the $PM_{2.5}$ concentrations of Baengnyeongdo due to the low self-emission and geographical location. In the case of urban areas with high self-emissions such as Seoul and Ulsan, the $PM_{2.5}$ contribution from overseas was relatively low compared to other regions, but the standard deviation of the season was relatively high. This study is expected to improve the understanding of the air pollutant phenomenon by analyzing the characteristics of $PM_{2.5}$ behavior in Northeast Asia according to the seasonal weather condition change. At the same time, this study can be used to establish the air quality policy in the future, knowing that the contribution of $PM_{2.5}$ concentration to the domestic and overseas can be different depending on the regional emission characteristics.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.2
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• pp.159-173
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• 2017

The Weather Research and Forecast (WRF) - Community Multiscale Air Quality (CMAQ) system was applied to investigate the influence of major point sources located in Chungcheongnam-do (CN) on surface $PM_{2.5}$ (Particulate Matter of which diameter is $2.5{\mu}m$ or less) concentrations in its surrounding areas. Uncertainties associated with contribution estimations were examined through cross-comparison of modeling results using various combinations of model inputs and setups; two meteorological datasets developed with WRF for 2010 and 2014, and two domestic emission inventories for 2010 and 2013 were used to estimate contributions of major point sources in CN. The results show that contributions of major point sources in CN to annual $PM_{2.5}$ concentrations over Seoul, Incheon, Gyeonggi, and CN ranged $0.51{\sim}1.63{\mu}g/m^3$, $0.71{\sim}1.62{\mu}g/m^3$, $0.63{\sim}1.66{\mu}g/m^3$, and $1.04{\sim}1.86{\mu}g/m^3$, respectively, depending on meteorology and emission inventory choice. It indicates that the contributions over the surrounding areas can be affected by model inputs significantly. Nitrate was the most dominant $PM_{2.5}$ component that was increased by major point sources in CN followed by sulfate, ammonium, and others. Based on the model simulations, it was estimated that primary $PM_{2.5}$ $(PPM)-to-PM_{2.5}$ conversion rates were 41.3~50.7 ($10^{-6}{\mu}g/m^3/TPY$) for CN, and 12.4~18.3 ($10^{-6}{\mu}g/m^3/TPY$) for Seoul, Incheon, and Gyeonggi, respectively. In addition, spatial gradients of PPM contributions show very steep trends. $NO_X$-to-nitrate conversion rates were 7.61~12.3 ($10^{-6}{\mu}g/m^3/TPY$) for CN, and 3.94~11.3 ($10^{-6}{\mu}g/m^3/TPY$) for the sub-regions in the SMA. $SO_2$-to-sulfate conversion rates were 4.04~5.28 ($10^{-6}{\mu}g/m^3/TPY$) for CN, and 3.73~4.43 ($10^{-6}{\mu}g/m^3/TPY$) for the SMA, respectively.

• Journal of Environmental Impact Assessment
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• v.27 no.2
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• pp.215-231
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• 2018

It was analyzed to identify the cause of $PM_{2.5}$ concentration changes for the fall in 2016 and 2017 in South Korea using ground measurement data such as meterological variables and $PM_{2.5}$, AOD from GOCI satellite, and WRF-CMAQ modeling system. The result of ground measurement data showed that the $PM_{2.5}$ concentrations for the fall in 2017 decreased by 12.3% ($3.0{\mu}g/m^3$) compared to that of 2016. The difference of $PM_{2.5}$ concentrations between 2016 and 2017 mainly occurred for 11 Oct. - 20 Oct. (CASE1) and 15 Nov. - 19 Nov. (CASE2) when weather conditions were difficult to long-range transport from foreign regions and favored atmospheric ventilation in 2017 compared to 2016. Simulated $PM_{2.5}$ concentrations in 2017 decreased by 64.0% ($23.1{\mu}g/m^3$) and 35.7% ($12.2{\mu}g/m^3$) during CASE1 and CASE2, respectively. These results corresponded to the changes in observed $PM_{2.5}$ concentrations such as 53.6% for CASE1 and 47.8% for CASE2. It is implied that the changes in weather conditions affected significantly the $PM_{2.5}$ concentrations for the fall between 2016 and 2017. The contributions to decreases in $PM_{2.5}$ concentrations was assessed as 52.8% by long-range transport from foreign regions and 47.2% by atmospheric ventilation effects in domestic regions during CASE1, whereas their decreases during CASE2 were affected by 66.4% from foreign regions and 33.6% in domestic regions.

• Journal of Korean Society on Water Environment
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• v.27 no.4
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• pp.467-474
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• 2011

In an effort to investigate water pollution characteristics of Juam lake, water samples were collected from three sites (Sites A, B, and C) of Oenam stream which is a typical tributary of rural watershed in the lake and analyzed for N concentration and the corresponding isotope ratio (${\delta}^{15}N$) of ${NO_3}^-$. Concentrations of ${NO_3}^-$ were not dramatically different among the sites; $0.8{\pm}0.2mgNL^{-1}$ (range: $0.0{\sim}4.3mgNL^{-1}$) for Site A, $1.1{\pm}0.2mgNL^{-1}$ ($0.0{\sim}4.3mgNL^{-1}$) for Site B, and $1.1{\pm}0.1mgNL^{-1}$ ($0.1{\sim}2.6mgNL^{-1}$) for Site C. Meanwhile, ${\delta}^{15}N$ tended to decrease with river flow; it was highest for Site A ($45.5{\pm}5.3$‰) followed by Site B ($19.7{\pm}2.0$‰) and Site C ($8.7{\pm}1.5$‰). Such high ${\delta}^{15}N$ values of ${NO_3}^-$ in Site A suggested that ${NO_3}^-$ derived from livestock feedlot (specifically livestock excrete of which ${\delta}^{15}N$ is higher than 10‰) is the predominant pollution sources despite mountainous area occupied the most of land-use in the watershed. Using the two-sources isotope mixing model, it was estimated that the contribution of cropping activities (i.e. fertilization) became greater in down-stream area (Sites B and C) due to the higher agricultural land-use than the up-stream area (Site A). Particularly, during the active cropping season, the low contribution of organic pollution sources indicated that domestic sewage was not the predominant pollution source. Therefore, it was suggested that agricultural sources such as livestock farming and cropping rather than mountainous and residential are the dominant sources of water pollution in the study area. These results could be effectively utilized in elucidating water pollution sources in rural areas and selecting water management practices.